References to the prior art are as follows:
[1] S. Haykin, Adaptive Filter Theory, 4th edition, 2002 (Chapter 2);
[2] P. Hoeher, S. Kaiser and P. Robertson, “TWO-DIMENSIONAL PILOT-SYMBOL-AIDED CHANNEL ESTIMATION BY WIENER FILTERING,” in Proc. IEEE ICASSP '97, Munich, Germany, pp. 18451848, Apr. 1997;
[3] F. Sanzi and J. Speidel, “An Adaptive Two-Dimensional Channel Estimator for Wireless OFDM with Application to Mobile DVB-T,” IEEE TRANSACTIONS ON BROADCASTING, VOL. 46, NO. 2, JUNE 2000;
[4] X. Hou, S. Li, D. Liu, C. Yin, G. Yue, “On two-dimensional adaptive channel estimation in OFDM systems,” IEEE 60th Vehicular Technology Conference, VTC2004-Fall, 2004;
[5] P. Schniter, “Low-complexity Equalization of OFDM in Doubly Selective Channels,” IEEE Trans. Signal Processing, Vol. 52, No.4, April, 2004, pp.1002-1011.
The content and technical spirit disclosed in the references may be used to clear the technical spirit of the present invention and may be included in the detailed description of the invention.
Since orthogonal frequency division multiplexing (OFDM) can overcome multi-path fading and can be efficiently embodied, it is widely used in wireless communication systems such as digital audio broadcasting (DAB), digital video broadcasting (DVB), and a wireless local area network (LAN). In order to enable an OFDM receiver to operate properly in an OFDM system, it is desirable for an equalizer in the OFDM receiver to accurately estimate a time-varying channel response.
Since a broadband fading channel can usually be viewed as a two-dimensional signal in time and frequency, the optimal solution to channel response estimation for a data cell based on scattered pilot cells may be two-dimensional (time and frequency) adaptive equalization discussed in References [1] and [2]. However, since a two-dimensional channel equalizer discussed in References [1] and [2] (hereinafter, referred to a 2D-Wiener equalizer) requires prior knowledge of the channel statistics in advance for channel response estimation, it may not be effective in a real communication environment. Moreover, the 2D-Wiener equalizer has problems of circuit complexity and power consumption, and therefore, it may not be suitable to commercial mass production of OFDM receivers.
FIG. 1 illustrates methods of estimating a channel response of a data cell using a conventional equalizer. Referring to FIG. 1, the equalizer estimates a channel response based on a predetermined number of scattered pilot cells which can be uniformly distributed in an OFDM signal in time and frequency domains. For example, in order to estimate a channel response for each of the data cells in a first OFDM symbol among a plurality of OFDM symbols, the equalizer may interpolate virtual pilot cells in the time domain based on the scattered pilot cells in the time domain in a first step 5. In a second step 15, the equalizer may interpolate in the frequency domain based on the channel responses of the respective virtual pilot cells and the scattered pilot cells in the frequency domain. However, the-above described one-dimensional equalizer, which performs one-dimensional channel equalization, may not be effective in an environment with time-frequency correlation, such as a mobile multi-path fading channel, and may have lower performance than the 2D-Wiener equalizer discussed in Reference [2].
Reference [3] discusses an equalizer which is made simpler than the 2D-Wiener equalizer discussed in Reference [2] by using filters in fixed time and frequency domains. The equalizer discussed in Reference [3] uses a filter adapted to Wiener filter theory in the worst scenario but does not show better performance than the 2D-Wiener equalizer discussed in Reference [2].
Adaptive two-dimensional channel estimation based on a two-dimensional least mean square (2D-LMS) algorithm does not need statistics of channels and can be effectively used for calculation of time-frequency correlation of a frequency response of a time-varying distributed fading channel. An equalizer using the 2D LMS algorithm (hereinafter, referred to as a 2D-LMS equalizer) is discussed in Reference [4]. The 2D-LMS equalizer can be more effective and designed more simply than the 2D-Wiener equalizer, but it may have greater complexity than the one-dimensional equalizer since it may use a large number of variable coefficients in the time and frequency domains. In other words, since the 2D-LMS equalizer uses many variable coefficients, it may perform equalization more slowly and may not detect quickly time-varying channel characteristics. Moreover, since the 2D-LMS equalizer requires a specially designed training sequence, it cannot be easily used in existing systems such as DVB-T or DVB-H.